näkemys - Computer Vision - # Semi-Supervised Change Detection in High-Resolution Remote Sensing Images

Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images

Q: How can the proposed C2F-SemiCD method be extended to handle multi-class change detection tasks in remote sensing imagery

The proposed C2F-SemiCD method can be extended to handle multi-class change detection tasks in remote sensing imagery by adapting the network architecture and loss functions to accommodate multiple classes. One approach could involve modifying the final layers of the network to output probabilities for each class, rather than just binary change/no-change predictions. This would require adjusting the loss function to incorporate multi-class classification metrics such as categorical cross-entropy. Additionally, the feature extraction modules within C2FNet could be enhanced to capture more diverse and nuanced features that are characteristic of different classes of changes. By training the model on datasets with multiple classes of changes and fine-tuning the architecture and training process, the C2F-SemiCD method can be effectively extended to handle multi-class change detection tasks in remote sensing imagery.

Q: What other semi-supervised learning techniques could be explored to further improve the performance of change detection models when labeled data is scarce

To further improve the performance of change detection models when labeled data is scarce, other semi-supervised learning techniques could be explored in conjunction with the C2F-SemiCD method. One potential technique is consistency regularization, where the model is trained to produce consistent predictions on perturbed versions of the same input. This can help the model learn more robust and generalizable features from both labeled and unlabeled data. Another approach is pseudo-labeling, where the model generates labels for unlabeled data based on its predictions. By iteratively updating the model with pseudo-labeled data, the model can learn from a larger pool of samples. Additionally, techniques like self-training, where the model iteratively refines its predictions on unlabeled data, and co-training, where multiple models are trained on different views of the data, can also be explored to enhance the performance of semi-supervised change detection models.

Q: What are the potential applications of the C2F-SemiCD method beyond remote sensing, such as in other computer vision tasks involving change detection

The C2F-SemiCD method has potential applications beyond remote sensing in other computer vision tasks involving change detection. One such application is in video surveillance, where the method can be used to detect changes in surveillance footage over time, such as the presence of new objects or movements in the scene. In medical imaging, the method can be applied to detect changes in patient scans over time, aiding in the diagnosis and monitoring of diseases. In autonomous driving, the method can be used for detecting changes in the environment, such as the appearance of new obstacles or road conditions, to improve the safety and decision-making of autonomous vehicles. Overall, the C2F-SemiCD method's ability to effectively extract change features from data can be leveraged in various computer vision tasks beyond remote sensing for accurate and efficient change detection.

Keskeiset käsitteet

A coarse-to-fine semi-supervised change detection method (C2F-SemiCD) is proposed, which effectively utilizes both labeled and unlabeled data to improve change detection performance in high-resolution remote sensing images.

Tiivistelmä

The paper presents a novel semi-supervised change detection approach called C2F-SemiCD, which consists of two key components:

C2FNet: A coarse-to-fine change detection network with a multi-scale attention mechanism. C2FNet gradually extracts change features from coarse to fine granularity through various modules, including multi-scale feature fusion, channel attention, spatial attention, global context, feature refinement, and feature aggregation.
Semi-supervised learning: C2F-SemiCD employs the mean teacher method for semi-supervised learning, where the teacher model generates pseudo-labels to guide the training of the student model (based on C2FNet). This allows the model to effectively leverage both labeled and unlabeled data.

The authors conduct extensive experiments on three prominent change detection datasets (GoogleGZ-CD, WHU-CD, LEVIR-CD) and perform detailed ablation studies. The results demonstrate that C2F-SemiCD significantly outperforms state-of-the-art supervised and semi-supervised change detection methods, especially when the proportion of labeled data is small (e.g., 5-30%). The proposed approach can effectively extract change features and achieve high-precision change detection, even with limited labeled samples.

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Tilastot

With only 5% labeled data, C2F-SemiCD achieves an F1-score of 80.93% on the GoogleGZ-CD dataset, outperforming the supervised-only C2FNet by 2.79%.
On the WHU-CD dataset, C2F-SemiCD with 30% labeled data achieves an F1-score of 92.85%, surpassing the supervised-only C2FNet with 100% labeled data by 1.49%.
For the LEVIR-CD dataset, C2F-SemiCD with 30% labeled data reaches an F1-score of 92.56%, compared to 94.36% for the supervised-only C2FNet with 100% labeled data.

Lainaukset

"A high-precision feature extraction model is crucial for change detection."
"Labelling samples of high-quality bi-temporal remote sensing images often need to compare the two images semantically and label the changed regions in the form of pixel level, which is very expensive and often time-consuming."
"With the help of only a small amount of labelled data, it combines unlabeled data with labelled data for analysis, so as to identify the change characteristics of the land surface more accurately."

Tärkeimmät oivallukset

C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images

by Chengxi Han,... klo arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13838.pdf

C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images

Syvällisempiä Kysymyksiä

How can the proposed C2F-SemiCD method be extended to handle multi-class change detection tasks in remote sensing imagery

The proposed C2F-SemiCD method can be extended to handle multi-class change detection tasks in remote sensing imagery by adapting the network architecture and loss functions to accommodate multiple classes. One approach could involve modifying the final layers of the network to output probabilities for each class, rather than just binary change/no-change predictions. This would require adjusting the loss function to incorporate multi-class classification metrics such as categorical cross-entropy. Additionally, the feature extraction modules within C2FNet could be enhanced to capture more diverse and nuanced features that are characteristic of different classes of changes. By training the model on datasets with multiple classes of changes and fine-tuning the architecture and training process, the C2F-SemiCD method can be effectively extended to handle multi-class change detection tasks in remote sensing imagery.

What other semi-supervised learning techniques could be explored to further improve the performance of change detection models when labeled data is scarce

To further improve the performance of change detection models when labeled data is scarce, other semi-supervised learning techniques could be explored in conjunction with the C2F-SemiCD method. One potential technique is consistency regularization, where the model is trained to produce consistent predictions on perturbed versions of the same input. This can help the model learn more robust and generalizable features from both labeled and unlabeled data. Another approach is pseudo-labeling, where the model generates labels for unlabeled data based on its predictions. By iteratively updating the model with pseudo-labeled data, the model can learn from a larger pool of samples. Additionally, techniques like self-training, where the model iteratively refines its predictions on unlabeled data, and co-training, where multiple models are trained on different views of the data, can also be explored to enhance the performance of semi-supervised change detection models.

What are the potential applications of the C2F-SemiCD method beyond remote sensing, such as in other computer vision tasks involving change detection

The C2F-SemiCD method has potential applications beyond remote sensing in other computer vision tasks involving change detection. One such application is in video surveillance, where the method can be used to detect changes in surveillance footage over time, such as the presence of new objects or movements in the scene. In medical imaging, the method can be applied to detect changes in patient scans over time, aiding in the diagnosis and monitoring of diseases. In autonomous driving, the method can be used for detecting changes in the environment, such as the appearance of new obstacles or road conditions, to improve the safety and decision-making of autonomous vehicles. Overall, the C2F-SemiCD method's ability to effectively extract change features from data can be leveraged in various computer vision tasks beyond remote sensing for accurate and efficient change detection.